An advanced deep reinforcement learning algorithm for three-layer D2D-edge-cloud computing architecture for efficient task offloading in the Internet of things

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dc.authorscopusidAli Ghaffari / 57197223215
dc.authorwosidAli Ghaffari / AAV-3651-2020
dc.contributor.authorMoghaddasi, Komeil
dc.contributor.authorRajabi, Shakiba
dc.contributor.authorGharehchopogh, Farhad Soleimanian
dc.contributor.authorGhaffari, Ali
dc.date.accessioned2025-04-18T07:36:59Z
dc.date.available2025-04-18T07:36:59Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractThe Internet of Things (IoTs) has transformed the digital landscape by interconnecting billions of devices worldwide, paving the way for smart cities, homes, and industries. With the exponential growth of IoT devices and the vast amount of data they generate, concerns have arisen regarding efficient task-offloading strategies. Traditional cloud and edge computing methods, paired with basic Machine Learning (ML) algorithms, face several challenges in this regard. In this paper, we propose a novel approach to task offloading in a Device-toDevice (D2D)-Edge-Cloud computing using the Rainbow Deep Q-Network (DQN), an advanced Deep Reinforcement Learning (DRL) algorithm. This algorithm utilizes advanced neural networks to optimize task offloading in the three-tier framework. It balances the trade-offs among D2D, Device-to-Edge (D2E), and Device/ Edge-to-Cloud (D2C/E2C) communications, benefiting both end users and servers. These networks leverage Deep Learning (DL) to discern patterns, evaluate potential offloading decisions, and adapt in real time to dynamic environments. We compared our proposed algorithm against other state -of -the -art methods. Through rigorous simulations, we achieved remarkable improvements across key metrics: an increase in energy efficiency by 29.8%, a 27.5% reduction in latency, and a 43.1% surge in utility.
dc.identifier.citationMoghaddasi, K., Rajabi, S., Gharehchopogh, F. S., & Ghaffari, A. (2024). An advanced deep reinforcement learning algorithm for three-layer D2D-edge-cloud computing architecture for efficient task offloading in the Internet of Things. Sustainable Computing: Informatics and Systems, 43, 100992.
dc.identifier.doi10.1016/j.suscom.2024.100992
dc.identifier.endpage18
dc.identifier.issn2210-5379
dc.identifier.issn2210-5387
dc.identifier.scopus2-s2.0-85192685663
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1016/j.suscom.2024.100992
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6463
dc.identifier.volume43
dc.identifier.wosWOS:001241264700001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorGhaffari, Ali
dc.institutionauthoridAli Ghaffari /0000-0001-5407-8629
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofSustainable computing-informatics & systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectInternet of Things
dc.subjectTask Offloading
dc.subjectDevice-to-Device Communications
dc.subjectEdge Computing
dc.subjectCloud Computing
dc.titleAn advanced deep reinforcement learning algorithm for three-layer D2D-edge-cloud computing architecture for efficient task offloading in the Internet of things
dc.typeArticle

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